GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration

arXiv:2404.09544v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design …

abstract accuracy adaptability applications arxiv consumption cost cs.ai cs.lg exploration gnns graph graph neural networks however memory memory consumption networks neural networks optimization solution training type via